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Tumor-associated antigen profiling in breast and ovarian cancer: mRNA, protein or T cell recognition?
Simone Kayser1, Iris Watermann, Christine Rentzsch
1Department of Gynecology and Obstetrics, University of Tübingen, Calwerstrasse 7, 72076, Tübingen, Germany.
Purpose:
The absence of tumor-associated antigens (TAA) which might elicit an immune response is one reason for the disappointing results of therapeutical vaccines in cancer patients. Moreover, impaired expression of MHC class-I and components involved in antigen processing, such as TAP-1, -2, LMP-2, -7, and MECL-1, may lead to tumor escape from immune recognition. Expression profiling of TAA is one approach towards the design of well-defined and individualized anti-tumor vaccines.
Methods:
Quantitative polymerase chain reaction (qRT-PCR) is the method of choice to characterize immunologically relevant properties of individual tumors. However, the application of qRT-PCR as a surrogate parameter for the expression of TAAs depends upon the assumption that the level of an mRNA species correlates with the cellular level of the protein it encodes. Therefore, we additionally analyzed TAA expression by immunofluorescence and T cell recognition.
Results:
In the present study we were unable to confirm that impaired TAP-1 or -2 (transporter associated with antigen processing) expression characterized at the mRNA level is an appropriate surrogate parameter for down-regulated MHC class-I expression in breast cancer. In addition, we analyzed the expression pattern of TAAs in breast and ovarian cancer cell lines. Besides the well-known over-expression of HER-2/neu, CEA, and MUC-1, multiple antigens of the MAGE-family were frequently co-expressed. We investigated whether detection of TAAs by qRT-PCR correlates with monoclonal antibody staining, and which method could predict T cell recognition. We demonstrated a correlation between tumor cell lysis by HLA-A*0201-restricted, MUC-1-specific CTL and threshold levels of MUC-1-specific mRNA.
Conclusion:
MUC-1 is an example that TAA profiling by RT-PCR and flow cytometry can fail to correlate with each other and are of limited value in the prediction of T cell recognition.
Insights
Tumor-associated antigen (TAA) profiling using RT-PCR and flow cytometry may not accurately predict T cell recognition. MUC-1 expression levels, for example, showed limited correlation, impacting anti-tumor vaccine development.
Area of Science:
- Cancer immunology
- Molecular oncology
- Vaccine development
Background:
- Therapeutic cancer vaccines face challenges due to the lack of tumor-associated antigens (TAAs) and impaired immune recognition.
- Tumor cells can evade immune detection through downregulated MHC class-I and antigen processing machinery (e.g., TAP-1, -2).
- TAA expression profiling is crucial for designing effective, individualized anti-tumor vaccines.
Purpose of the Study:
- To evaluate quantitative polymerase chain reaction (qRT-PCR) as a surrogate for TAA expression and its correlation with protein levels and T cell recognition.
- To investigate the utility of TAA profiling in breast and ovarian cancer for predicting immune response.
Main Methods:
- Quantitative polymerase chain reaction (qRT-PCR) was used to analyze TAA mRNA expression.
- Immunofluorescence and T cell recognition assays were employed to validate qRT-PCR findings.
- Monoclonal antibody staining was performed to assess protein expression.
Main Results:
- Impaired TAP-1 or -2 mRNA expression did not reliably correlate with downregulated MHC class-I expression in breast cancer.
- MAGE-family antigens were frequently co-expressed with known TAAs like HER-2/neu, CEA, and MUC-1 in breast and ovarian cancer cell lines.
- A correlation was observed between MUC-1-specific mRNA levels and tumor cell lysis by MUC-1-specific CTLs.
Conclusions:
- TAA profiling by RT-PCR and flow cytometry may not always correlate.
- These methods have limited value in predicting T cell recognition for anti-tumor vaccine strategies.
- MUC-1 serves as an example where TAA profiling requires careful interpretation regarding its predictive power for T cell responses.
